modeling the logistics sector in catalonia...maz nadjm mit influencer forbes top 50 social media...

Modeling the Logistics Sector in CataloniaCarlos Carrasco

Researcher – IESE Business School

Smart City Expo World Congress - November 2019

2IESE Business School Modeling the Logistics Sector in Catalonia.

Introduction

Maz Nadjm

MIT Influencer

Forbes Top 50

Social Media

Influencers

UK Head of

Global Media

Ogilvy Group


Cornelia Yzer


Introduction


Introduction

The world got flat… [obsolescing]

geography, distanceor, in the nearfuture, evenlanguage.


Introduction

The world got flat… [obsolescing]

geography, distanceor, in the nearfuture, evenlanguage.

The reality is that

DISTANCE MATTERS…

A LOT


Introduction


Introduction

Catalonia is the main logistics center not only inSpain, but throughout southern Europe and theMediterranean due to its strategic location, itscomplete infrastructure network and the highlevel of service provision of logistics companies.


Introduction


The economic weight of the logistics sector inthe Catalan economy as a whole is quitesignificant. According to Pimec Logística (2018),in 2018, the logistics sector represented 12.3%of Catalan GDP.


Introduction


The economic weight of the logistics sector inthe Catalan economy as a whole is quitesignificant. According to Pimec Logística (2018),in 2018, the logistics sector represented 12.3%of Catalan GDP.

The activities of transport and storage land, seaand air amounted to more than 27,000 millioneuros.


Introduction

HOWEVER…

YOU CANNOT MANAGE

WHAT YOU CANNOT MEASURE


Introduction

This project, based on the actual (observed) data, allows us to obtain an overview

of the movement of goods in Catalonia by adding detailed information available at

the road segment level for a better understanding of Catalan logistics.

The ultimate goal is to allow an improvement in decision making.

15IESE Business School A Way to Learn. A Mark to Make. A World to Change.

CONTEXTUALDATA


Balance of TradeContextual Data

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Importacíon Exportacíon

Fuente: Idescat. (2019). Datos del COMEST (Comercio con el extranjero).

Balanza comercial de Cataluña (2015-2018)

The volume of imports,

in tons, was higher than

that of exports during

the period analyzed in

that report.

Imports were 1.5 times

higher than exports

during those four years,

thus presenting a deficit

balance.


Balance of Trade by Transport TypeContextual Data


Transporte por ferrocarriles en Cataluña (2015-2018)

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Transporte por carretera en Cataluña (2015-2018)

00,511,522,533,544,5

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Transporte aéreo en Cataluña (2015-2018)

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Transporte marítimo en Cataluña (2015-2018)

By road and by air, the volume of

imports and exports is very

similar. That is, the trade balance

of products transported by road and

by air has a balanced balance.


Balance of Trade by Transport TypeContextual Data


Transporte por ferrocarriles en Cataluña (2015-2018)

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Transporte por carretera en Cataluña (2015-2018)

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Transporte aéreo en Cataluña (2015-2018)

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Transporte marítimo en Cataluña (2015-2018)

In the case of rail and -mainly-

maritime transport, there is a

greater volume of imports

than exports.


Balance of Trade by ProvinceContextual Data


Balanza comercial de Barcelona (2015-2018)

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Balanza comercial de Girona (2015-2018)

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Balanza comercial de Lleida (2015-2018)

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Balanza comercial de Tarragona (2015-2018)

While Barcelona and Tarragona (both with important ports) present the

same pattern of the Catalan aggregate scenario, that is, imports exceed

exports in tons…


Balance of Trade by ProvinceContextual Data


Balanza comercial de Barcelona (2015-2018)

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Balanza comercial de Girona (2015-2018)

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Balanza comercial de Lleida (2015-2018)

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Balanza comercial de Tarragona (2015-2018)

…Girona and Lleida (without access to the sea) show an opposite

evolution. In these two provinces, the trade balance measured in tons is

inverted and presents a deficit balance.


AP-2 and AP-7Contextual Data

Número de camiones entre Cataluña y Aragón AP-2 (2016-2018)

Fuente: Abertis. (2019). Datos proporcionados por la empresa.

It is observed that the number of trucks that move between Catalonia and Aragon

varies (in working days) from around 800 vehicles per day at the beginning of 2016 to

1,400 at the end of 2018.


It is observed that the number of trucks that move between Catalonia and Aragon

varies (in working days) from around 800 vehicles per day at the beginning of 2016 to

1,400 at the end of 2018.


Número de camiones entre Cataluña y Aragón AP-2 (2016-2018)


If a shorter period is analyzed in detail, we

can see the traffic fall on the AP-2

motorway on Saturday and, especially, on

Sunday.

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Zoom Septiembre 2016



Número de camiones entre Cataluña y Valencia AP-7 (2016-2018)


Número de camiones entre Cataluña y Francia AP-7 (2016-2018)

Although they show a

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traffic volume is four

times more intense in

the France direction

than towards Valencia.


C-16, C-17, C-25 and C-35Contextual Data

Número de camiones en C-17 (2015-2018)

Fuente: Cedinsa. (2019). Datos proporcionados por la empresa.



In the figures based on the data provided by

Cedinsa we see the same pattern of truck

movement that was observed in the AP-7 and AP-

2, that is, a constant level with a slight upward

trend (more intense in the case of C-25) during

the last years and seasonal falls in January and

August.





The graph presents the weekly pattern already observed of truck movement

on the roads. Both on the roads managed by Abertis and those managed by

Cedinsa, during weekends, truck traffic is reduced by more than 70%.


METHODOLOGY


In some selected points of the road network of Catalonia there are stations that

count the number of trucks that pass daily through that section. Cedinsa and the

Generalitat of Catalonia were the main providers of this data. The ports of Barcelona

and Tarragona also have counters at their main exterior doors.

Traffic FlowMethodology


Traffic FlowMethodology

Abertis manages the AP-7 and AP-2 toll roads, and both have toll stations. We

have used the data obtained by adding the count of trucks that pass through

each pair of stations combining these flows of origin-destination with the total

number of trucks that pass through a given point.


Geolocated DataMethodology

To connect all the information and represent it later on a real map, a road frame wasneeded. The road network was downloaded from the opensource OpenStreetMap(OSM) project, in ESRI-Shapefile format.

The data not only contains the geolocation of the roads, but also has other relevantinformation, such as maximum speeds, type and road reference, etc.


Geolocated DataMethodology

We use this data to build a graph with nodes and arcs that connect all A-B origins anddestinations (municipalities and entrances and exits), with innovative methods and ourown functions, to preserve directional information and connectivity.


Shortest PathsContextual Data

𝐼 𝐴 → B || 𝑃1 = [1De, 1Iz, 3De, 2Iz, 1De, 1Iz, 1De]



𝐼 𝐴 → B || 𝑃1 = [1De, 1Iz, 3De, 2Iz, 1De, 1Iz, 1De] 𝐼 𝐴 → B || 𝑃2 = [6De, 3Iz]



To represent and geolocate each route of A B (whatever the two points in the

network), all possible routes had to be evaluated. Each route chosen was the

result of minimizing the amount of time it takes for a truck to cross it when

there is no traffic (so that the speed limit for heavy vehicles given by road data

can be applied).

The result of calculating the algorithm for each route of A-B is the matrix of

shorter routes M. For example, if the shortest route (SP) of the route from La

Junquera to Barcelona passes through the AP-7 road, the arcs that represent the

part of the AP-7 used will be equal to 1, and 0 for which represent all the roads

that were not used. Formally represented as follows:


EstimationsMethodology

BAGiven these data, we had to make an

inference about the actual traffic that

goes from A B every day.

For each day, we perform a different

estimation process, using a linear

programming model.

BABABA

…



BAGiven these data, we had to make an

inference about the actual traffic that

goes from A B every day.

For each day, we perform a different

estimation process, using a linear

programming model.

BABABA

…

THESE OPERATIONS RESULT IN MORE THAN

5.000.000DATA POINTS



IMPORTANT NOTE: Since the model tries to distribute all the trucks that pass through the

borders and the origin and destination data refers only to foreign trade,

internal traffic is invisible

The Observatori de la Logística de Cimalsa estimated, in 2018, that internal truck traffic in

Catalonia is around 45% of total traffic, so the results we will present should be interpreted

as corresponding to the 55% of total truck traffic.


RESULTS


Overall ResultsResults

One of the most important results of the model is

that traffic is very clearly concentrated in the first

four positions in a quasi-exponential distribution.


Overall ResultsResults Densidad de carreteras por número total de camiones estimados

This means that much of the

internal traffic has a special

presence on roads such as the B-

10 (Ronda Litoral de Barcelona),

the AP-7, the C33 and the N-2.



The seven road segments corresponding to the

B-10 accumulate more than 5,000,000 trucks

during the 2016-2018 period



The B-10 stands out from the rest of the roads, especially between 2017 and mid-2018.



In contrast, as of mid-2018, the C-33 takes on special importance, although always below

the B-10 values.



The N-2 and C-25 also follow an upward trend, although at a lower speed. Finally, the AP-7 remains

relatively stable over the years, with values ranging between 7,000 and 10,000 trucks per month.


Entries and Departures through Catalan bordersResults - Trucks heading to France

Snow Storm

(AP-7 was closed)


Entries and Departures through Catalan bordersResults – Trucks coming from France


Entries and Departures through Catalan bordersResults – Trucks heading to Aragón


Entries and Departures through Catalan bordersResults – Trucks coming from Aragón


Entries and Departures through Catalan bordersResults – Trucks heading to Valencia


Entries and Departures through Catalan bordersResults – Trucks coming from Valencia


Entries and Departures through Catalan bordersResults – Trucks heading to Tarragona’s port


Entries and Departures through Catalan bordersResults – Trucks coming from Tarragona’s port


Entries and Departures through Catalan bordersResults – Trucks heading to Barcelona’s port


Entries and Departures through Catalan bordersResults – Trucks coming from Barcelona’s port


Interactive MapGeneral Results


Interactive MapFilter by Date


Interactive MapFilter by Origin


Interactive MapRoad Segment Information


CONCLUDINGREMARKS


Concluding Remarks

The contribution of the project is twofold:

• Determining the number of trucks that circulate on each segment on a daily basis.

Although it might seem obvious that some roads were the main ones for the transport of

goods, thanks to the model, the number of trucks that circulate on them can be established

and thus compare the magnitude of the differences between the different roads.

• Granularity: as we have stated previously, the Catalan road structure is made up of more

than 5,000 sections of road, so that, for one person, it is impossible to know each and every

one of the sections . Thanks to modeling, we have created a database that can be explored

in detail, municipality by municipality, section by section, to identify potentialities, possible

problems and contextualize existing data through comparatives. This is of great importance

for decision making.


Future Steps

This project is a demonstration that we achieve a greater knowledge about our

infrastructure based on new methodologies like Big Data or Machine Learning.

Nowadays we have

greater computing capacity,

greater data availability

and

improvements in the calculation processes and

algorithms.


Future Steps

This opens the doors to analysis that were previously impossible to perform due to lack of

resources in one of the three aspects indicated. Therefore, we propose the following steps:

The benefits obtained with this type of analysis far outweigh the costs of abandoning old information

systems and adopting new methodologies based on big data, artificial intelligence or machine learning.

Geographic expansion of

the model

Better data:

Internet of ThingsMore data:

GPS traces


Future Steps

The time has come for

organizations to go one step further

and focus on

data-driven management

modeling the logistics sector in catalonia...maz nadjm mit influencer forbes top 50 social media...

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